Chen, L., Magliano, D. J, Balkau, B., Wolfe, R., Brown, L., Tonkin, A. M, Zimmet, P. Z & Shaw, JE. (2011). Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: The AusDiab study. Diabetic Medicine,289(4), 414-423. United Kingdom: Wiley-Blackwell Publishing Ltd.. Retrieved from https://doi.org/10.1111/j.1464-5491.2010.03188.x
Aims To evaluate how to most efficiently screen populations to detect people at high risk of incident Type 2 diabetes and those with prevalent, but undiagnosed, Type 2 diabetes. Methods Data from 5814 adults in the Australian Diabetes, Obesity and Lifestyle study were used to examine four different types of screening strategies. The strategies incorporated various combinations of cut-points of fasting plasma glucose, the non-invasive Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK1) and a modified version of the tool incorporating fasting plasma glucose (AUSDRISK2). Sensitivity, specificity, positive predictive value, screening costs per case of incident or prevalent undiagnosed diabetes identified and intervention costs per case of diabetes prevented or reverted were compared. Results Of the four strategies that maximized sensitivity and specificity, use of the non-invasive AUSDRISK1, followed by AUSDRISK2 in those found to be at increased risk on AUSDRISK1, had the highest sensitivity (80.3%; 95% confidence interval 76.6–84.1%), specificity (78.1%; 95% confidence interval 76.9–79.2%) and positive predictive value (22.3%; 95% confidence interval 20.2–24.4%) for identifying people with either prevalent undiagnosed diabetes or future incident diabetes. It required the fewest people (24.1%; 95% confidence interval 23.0–25.2%) to enter lifestyle modification programmes, and also had the lowest intervention costs and combined costs of running screening and intervention programmes per case of diabetes prevented or reverted. Conclusions Using a self-assessed diabetes risk score as an initial screening step, followed by a second risk score incorporating fasting plasma glucose, would maximize efficiency of identifying people with undiagnosed Type 2 diabetes and those at high risk of future diabetes.
Mary MacKillop Institute for Health Research
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